Low perfusion signal processing systems and methods

ABSTRACT

In some embodiments, systems and methods for identifying a low perfusion condition are provided by transforming a signal using a wavelet transform to generate a scalogram. A pulse band and adjacent marker regions in the scalogram are identified. Characteristics of the marker regions are used to detect the existence of a lower perfusion condition. If such a condition is detected, an event may be triggered, such as an alert or notification.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/215,349, filed Aug. 23, 2011, which is a divisional of U.S. patentapplication Ser. No. 12/249,325, filed Oct. 10, 2008, now U.S. Pat. No.8,082,110, which claims priority to U.S. Provisional Application No.61/080,977, filed Jul. 15, 2008, all of which are incorporated byreference herein in their entireties.

SUMMARY

The present disclosure relates to signal processing and, moreparticularly, the present disclosure relates to using continuous wavelettransforms for processing, for example, a photoplethysmograph (PPG)signal. PPG signals are used in a variety of fields, including inmedical monitoring devices, such as a pulse oximeter. A pulse oximeteris a device that is capable of indirectly measuring blood oxygensaturation and is typically used by healthcare providers as a monitoringdevice for patients. The oximeter generally uses a light emitter thatshines through a monitoring site or point on a patient. A photodetectoror other sensor may be used to receive the light that has passed throughthe monitoring site. The light passing through the site may be measuredand analyzed to determine the patient's blood oxygen saturation using,for example, a scalogram generated by wavelet-transformation of the PPGsignal.

Since oxygen is critical to sustain human life, monitoring patients'blood oxygen saturation is one important indicator of a patient'sphysiological condition. If blood oxygen saturation levels determined bythe oximeter are low, out of range, above or below a certain threshold,this may be an indication that the patient is generally experiencing lowperfusion, high vascular peripheral resistance, or other condition, orthat the monitoring site is locally experiencing low perfusion, highvascular peripheral resistance, or other condition. Certain illnesses orphysiological conditions may cause low perfusion, and high peripheralresistance. Low perfusion may also be caused (or worsened) by patientposition, or external factors.

In the various embodiments disclosed herein, features of a PPG scalogramare analyzed to determine whether the monitored patient is experiencinglow perfusion or high vascular peripheral resistance. When low perfusionor high vascular peripheral resistance is detected, a corrective actionmay be triggered. The corrective action may include an alert to examinethe patient, reposition a sensor, use a second sensor, or other action.

One way to perform the analysis may include identifying features of thescalogram, for example, marker regions, and residual markers locatednear a pulse band. Another technique may be provided by comparingfeatures of the scalogram against, for example, selectable thresholds,other scalograms having known and distinct aspects and features, orother comparative elements. These techniques are further describedherein. Although the embodiments herein are discussed in reference touse with a pulse oximeter, they are equally applicable to other types ofdevices, including continuous non-invasive blood pressure (CNIBP)measurement devices. Systems and methods for calculating CNIBP aredescribed in Chen et al. U.S. Pat. No. 6,566,251 and Sethi et al. U.S.patent application Ser. No. 12/242,238, entitled “SYSTEMS AND METHODSFOR NON-INVASIVE BLOOD PRESSURE MONITORING,” filed Sep. 30, 2008 (DocketNo. H-RM-01205-1 (COV-11-01)), both of which are incorporated byreference herein in their entireties.

An embodiment is provided by a method comprising receiving a signal thatmay be transformed using a wavelet transform. The transformed signal maybe used to generate a scalogram. A pulse band and a marker regionadjacent to the pulse band in the scalogram may be identified. Acharacteristic of the marker region may be identified and used as abasis for determining that a low perfusion condition exists. If suchcondition exists, an event may trigger. The marker region may beidentified using ridges or modulus maxima of the scalogram. Somecharacteristics of the marker region include: a change in energy withinthe marker region over time, a change in amplitude within the markerregion over time, a residual marker, a number of residual markers, asize of the residual marker, a location of the residual marker, energyof the residual marker, amplitude of the residual marker, and strengthof the residual marker. The characteristics may be compared with athreshold, which may cause the event to trigger. The threshold may bebased in part on a user classification. The user classification may alsobe used as a basis for triggering the event. Some types of eventsinclude: sending a control signal to a display, sending a control signalto a speaker, generating an alert, sending a control signal to a secondsensor, and moving a sensor. Some examples of alerts include: anindication of a low perfusion condition, an indication to examine apatient, an indication to move the sensor, an indication to move thesensor closer to an artery, an indication to move the sensor away froman artery, and an indication of use of a second sensor. In someembodiments, a second scalogram may be generated and compared againstthe original scalogram.

In another embodiment, a system is provided comprising: a signalgenerator for generating a signal, a processor coupled to the signalgenerator, and a display. The processor is capable of transforming thesignal using a wavelet transform. The transformed signal may be used asa basis for generating a scalogram. A pulse band and marker regionadjacent to the pulse band in the scalogram may be identified by theprocessor. The processor is also capable of identifying a characteristicof the marker region, which may be used for determining that a lowperfusion condition exists. The processor may also trigger an event.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative pulse oximetry system in accordance with anembodiment;

FIG. 2 is a block diagram of the illustrative pulse oximetry system ofFIG. 1 coupled to a patient in accordance with an embodiment;

FIGS. 3( a) and 3(b) show illustrative views of a scalogram derived froma PPG signal in accordance with an embodiment;

FIG. 3( c) shows an illustrative scalogram derived from a signalcontaining two pertinent components in accordance with an embodiment;

FIG. 3( d) shows an illustrative schematic of signals associated with aridge in FIG. 3( c) and illustrative schematics of a further waveletdecomposition of these newly derived signals in accordance with anembodiment;

FIGS. 3( e) and 3(f) are flow charts of illustrative steps involved inperforming an inverse continuous wavelet transform in accordance withembodiments;

FIG. 4 is a block diagram of an illustrative continuous waveletprocessing system in accordance with some embodiments;

FIG. 5 shows an illustrative method for identifying a low perfusioncondition in accordance with an embodiment.

FIG. 6( a) shows a plot of a pulse signal and scalogram in accordancewith an embodiment;

FIG. 6( b) shows a plot of energy density in accordance with anembodiment;

FIG. 6( c) shows a plot of representative energy in accordance with anembodiment;

FIG. 6( d) shows an illustrative scalogram derived from a PPG signal inaccordance with an embodiment; and

FIG. 7 shows an illustrative method for identifying a characteristic ofa marker region in accordance with an embodiment.

DETAILED DESCRIPTION

In medicine, a plethysmograph is an instrument that measuresphysiological parameters, such as variations in the size of an organ orbody part, through an analysis of the blood passing through or presentin the targeted body part, or a depiction of these variations. Anoximeter is an instrument that may determine the oxygen saturation ofthe blood. One common type of oximeter is a pulse oximeter, whichdetermines oxygen saturation by analysis of an optically sensedplethysmograph.

A pulse oximeter is a medical device that may indirectly measure theoxygen saturation of a patient's blood (as opposed to measuring oxygensaturation directly by analyzing a blood sample taken from the patient)and changes in blood volume in the skin. Ancillary to the blood oxygensaturation measurement, pulse oximeters may also be used to measure thepulse rate of the patient. Pulse oximeters typically measure and displayvarious blood flow characteristics including, but not limited to, theoxygen saturation of hemoglobin in arterial blood.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may pass light using a lightsource through blood perfused tissue and photoelectrically sense theabsorption of light in the tissue. For example, the oximeter may measurethe intensity of light that is received at the light sensor as afunction of time. A signal representing light intensity versus time or amathematical manipulation of this signal (e.g., a scaled versionthereof, a log taken thereof, a scaled version of a log taken thereof,etc.) may be referred to as the photoplethysmograph (PPG) signal. Inaddition, the term “PPG signal,” as used herein, may also refer to anabsorption signal (i.e., representing the amount of light absorbed bythe tissue) or any suitable mathematical manipulation thereof. The lightintensity or the amount of light absorbed may then be used to calculatethe amount of the blood constituent (e.g., oxyhemoglobin) being measuredas well as the pulse rate and when each individual pulse occurs.

The light passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent in the tissue and the related lightabsorption. Red and infrared wavelengths may be used because it has beenobserved that highly oxygenated blood will absorb relatively less redlight and more infrared light than blood with a lower oxygen saturation.By comparing the intensities of two wavelengths at different points inthe pulse cycle, it is possible to estimate the blood oxygen saturationof hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased on Lambert-Beer's law. The following notation will be used herein:

I(λ,t)=I _(o)(λ)exp(−(sβ _(o)(λ)+(1−s)β_(r)(λ))l(t))  (1)

where:λ=wavelength;t=time;I=intensity of light detected;I_(o)=intensity of light transmitted;s=oxygen saturation;β_(o), β_(r)=empirically derived absorption coefficients; and1(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., red and infrared (IR)), and then calculates saturation by solvingfor the “ratio of ratios” as follows.

1. First, the natural logarithm of (1) is taken (“log” will be used torepresent the natural logarithm) for IR and Red

log I=log I _(o)−(sβ _(o)+(1−s)β_(r))l  (2)

2. (2) is then differentiated with respect to time

$\begin{matrix}{\frac{{\log}\; I}{t} = {{- \left( {{s\; \beta_{o}} + {\left( {1 - s} \right)\beta_{r}}} \right)}\frac{l}{t}}} & (3)\end{matrix}$

3. Red (3) is divided by IR (3)

$\begin{matrix}{\frac{{\log}\; {{I\left( \lambda_{R} \right)}/{t}}}{{\log}\; {{I\left( \lambda_{IR} \right)}/{t}}} = \frac{{s\; {\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\; {\beta_{o}\left( \lambda_{IR} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{IR} \right)}}}} & (4)\end{matrix}$

4. Solving for s

$s = \frac{{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}{\beta_{r}\left( \lambda_{IR} \right)}}}{\begin{matrix}{{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} -} \\{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}$

Note in discrete time

$\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \; {I\left( {\lambda,t_{2}} \right)}} - {\log \; {I\left( {\lambda,t_{1}} \right)}}}$

Using log A-log B=log A/B,

$\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {\log \left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}$

So, (4) can be rewritten as

$\begin{matrix}{{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\log \left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log \left( \frac{I\left( {t_{1},\lambda_{IR}} \right)}{I\left( {t_{2},\lambda_{IR}} \right)} \right)}} = R} & (5)\end{matrix}$

where R represents the “ratio of ratios.” Solving (4) for s using (5)gives

$s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\; {\beta_{r}\left( \lambda_{IR} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{IR} \right)} - {\beta_{r}\left( \lambda_{IR} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}$

From (5), R can be calculated using two points (e.g., PPG maximum andminimum), or a family of points. One method using a family of pointsuses a modified version of (5). Using the relationship

$\begin{matrix}{\frac{{\log}\; I}{t} = \frac{{I}/{t}}{I}} & (6)\end{matrix}$

now (5) becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{IR} \right)}}{t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}}{I\left( {t_{1},\lambda_{IR}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{IR}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{IR}} \right)} - {I\left( {t_{1},\lambda_{IR}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{R}} \right)}}} \\{= R}\end{matrix} & (7)\end{matrix}$

which defines a cluster of points whose slope of y versus x will give Rwhere

x(t)=[I(t ₂,λ_(IR))−I/(t ₁,λ_(IR))]I(t ₁,λ_(R))

y(t)=[I(t ₂,λ_(R))−I/(t ₁,λ_(R))]I(t ₁,λ_(IR))

y(t)=Rx(t)  (8)

FIG. 1 is a perspective view of an embodiment of a pulse oximetry system10. System 10 may include a sensor 12 and a pulse oximetry monitor 14.Sensor 12 may include an emitter 16 for emitting light at two or morewavelengths into a patient's tissue. A detector 18 may also be providedin sensor 12 for detecting the light originally from emitter 16 thatemanates from the patient's tissue after passing through the tissue.

According to another embodiment and as will be described, system 10 mayinclude a plurality of sensors forming a sensor array in lieu of singlesensor 12. Each of the sensors of the sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of the array may be charged coupled device (CCD) sensor. Inanother embodiment, the sensor array may be made up of a combination ofCMOS and CCD sensors. The CCD sensor may comprise a photoactive regionand a transmission region for receiving and transmitting data whereasthe CMOS sensor may be made up of an integrated circuit having an arrayof pixel sensors. Each pixel may have a photodetector and an activeamplifier.

According to an embodiment, emitter 16 and detector 18 may be onopposite sides of a digit such as a finger or toe, in which case thelight that is emanating from the tissue has passed completely throughthe digit. In an embodiment, emitter 16 and detector 18 may be arrangedso that light from emitter 16 penetrates the tissue and is reflected bythe tissue into detector 18, such as a sensor designed to obtain pulseoximetry data from a patient's forehead.

In an embodiment, the sensor or sensor array may be connected to anddraw its power from monitor 14 as shown. In another embodiment, thesensor may be wirelessly connected to monitor 14 and include its ownbattery or similar power supply (not shown). Monitor 14 may beconfigured to calculate physiological parameters based at least in parton data received from sensor 12 relating to light emission anddetection. In an alternative embodiment, the calculations may beperformed on the monitoring device itself and the result of the oximetryreading may be passed to monitor 14. Further, monitor 14 may include adisplay 20 configured to display the physiological parameters or otherinformation about the system. In the embodiment shown, monitor 14 mayalso include a speaker 22 to provide an audible sound that may be usedin various other embodiments, such as for example, sounding an audiblealarm in the event that a patient's physiological parameters are notwithin a predefined normal range.

In an embodiment, sensor 12, or the sensor array, may be communicativelycoupled to monitor 14 via a cable 24. However, in other embodiments, awireless transmission device (not shown) or the like may be used insteadof or in addition to cable 24.

In the illustrated embodiment, pulse oximetry system 10 may also includea multi-parameter patient monitor 26. The monitor may be cathode raytube type, a flat panel display (as shown) such as a liquid crystaldisplay (LCD) or a plasma display, or any other type of monitor nowknown or later developed. Multi-parameter patient monitor 26 may beconfigured to calculate physiological parameters and to provide adisplay 28 for information from monitor 14 and from other medicalmonitoring devices or systems (not shown). For example, multiparameterpatient monitor 26 may be configured to display an estimate of apatient's blood oxygen saturation generated by pulse oximetry monitor 14(referred to as an “SpO₂” measurement), pulse rate information frommonitor 14 and blood pressure from a blood pressure monitor (not shown)on display 28.

Monitor 14 may be communicatively coupled to multi-parameter patientmonitor 26 via a cable 32 or 34 that is coupled to a sensor input portor a digital communications port, respectively and/or may communicatewirelessly (not shown). In addition, monitor 14 and/or multi-parameterpatient monitor 26 may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14may be powered by a battery (not shown) or by a conventional powersource such as a wall outlet.

FIG. 2 is a block diagram of a pulse oximetry system, such as pulseoximetry system 10 of FIG. 1, which may be coupled to a patient 40 inaccordance with an embodiment. Certain illustrative components of sensor12 and monitor 14 are illustrated in FIG. 2. Sensor 12 may includeemitter 16, detector 18, and encoder 42. In the embodiment shown,emitter 16 may be configured to emit at least two wavelengths of light(e.g., RED and IR) into a patient's tissue 40. Hence, emitter 16 mayinclude a RED light emitting light source such as RED light emittingdiode (LED) 44 and an IR light emitting light source such as IR LED 46for emitting light into the patient's tissue 40 at the wavelengths usedto calculate the patient's physiological parameters. In one embodiment,the RED wavelength may be between about 600 nm and about 700 nm, and theIR wavelength may be between about 800 nm and about 1000 nm. Inembodiments where a sensor array is used in place of single sensor, eachsensor may be configured to emit a single wavelength. For example, afirst sensor emits only a RED light while a second only emits an IRlight.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detector 18 may be chosen to bespecifically sensitive to the chosen targeted energy spectrum of theemitter 16.

In an embodiment, detector 18 may be configured to detect the intensityof light at the RED and IR wavelengths. Alternatively, each sensor inthe array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe RED and IR wavelengths in the patient's tissue 40.

In an embodiment, encoder 42 may contain information about sensor 12,such as what type of sensor it is (e.g., whether the sensor is intendedfor placement on a forehead or digit) and the wavelengths of lightemitted by emitter 16. This information may be used by monitor 14 toselect appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This information mayallow monitor 14 to determine, for example, patient-specific thresholdranges in which the patient's physiological parameter measurementsshould fall and to enable or disable additional physiological parameteralgorithms. Encoder 42 may, for instance, be a coded resistor whichstores values corresponding to the type of sensor 12 or the type of eachsensor in the sensor array, the wavelengths of light emitted by emitter16 on each sensor of the sensor array, and/or the patient'scharacteristics. In another embodiment, encoder 42 may include a memoryon which one or more of the following information may be stored forcommunication to monitor 14: the type of the sensor 12; the wavelengthsof light emitted by emitter 16; the particular wavelength each sensor inthe sensor array is monitoring; a signal threshold for each sensor inthe sensor array; any other suitable information; or any combinationthereof.

In an embodiment, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to a light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for the RED LED 44and the IR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through an amplifier 62 and a switching circuit 64. Thesesignals are sampled at the proper time, depending upon which lightsource is illuminated. The received signal from detector 18 may bepassed through an amplifier 66, a low pass filter 68, and ananalog-to-digital converter 70. The digital data may then be stored in aqueued serial module (QSM) 72 (or buffer) for later downloading to RAM54 as QSM 72 fills up. In one embodiment, there may be multiple separateparallel paths having amplifier 66, filter 68, and A/D converter 70 formultiple light wavelengths or spectra received.

In an embodiment, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂ and pulse rate, using variousalgorithms and/or look-up tables based on the value of the receivedsignals and/or data corresponding to the light received by detector 18.Signals corresponding to information about patient 40, and particularlyabout the intensity of light emanating from a patient's tissue overtime, may be transmitted from encoder 42 to a decoder 74. These signalsmay include, for example, encoded information relating to patientcharacteristics. Decoder 74 may translate these signals to enable themicroprocessor to determine the thresholds based on algorithms orlook-up tables stored in ROM 52. User inputs 56 may be used to enterinformation about the patient, such as age, weight, height, diagnosis,medications, treatments, and so forth. In an embodiment, display 20 mayexhibit a list of values which may generally apply to the patient, suchas, for example, age ranges or medication families, which the user mayselect using user inputs 56.

The optical signal through the tissue can be degraded by noise, amongother sources. One source of noise is ambient light that reaches thelight detector. Another source of noise is electromagnetic coupling fromother electronic instruments. Movement of the patient also introducesnoise and affects the signal. For example, the contact between thedetector and the skin, or the emitter and the skin, can be temporarilydisrupted when movement causes either to move away from the skin. Inaddition, because blood is a fluid, it responds differently than thesurrounding tissue to inertial effects, thus resulting in momentarychanges in volume at the point to which the oximeter probe is attached.

Noise (e.g., from patient movement) can degrade a pulse oximetry signalrelied upon by a physician, without the physician's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the doctor is watching the instrument orother parts of the patient, and not the sensor site. Processing pulseoximetry (i.e., PPG) signals may involve operations that reduce theamount of noise present in the signals or otherwise identify noisecomponents in order to prevent them from affecting measurements ofphysiological parameters derived from the PPG signals.

It will be understood that the present disclosure is applicable to anysuitable signals and that PPG signals are used merely for illustrativepurposes. Those skilled in the art will recognize that the presentdisclosure has wide applicability to other signals including, but notlimited to other biosignals (e.g., electrocardiogram,electroencephalogram, electrogastrogram, electromyogram, heart ratesignals, pathological sounds, ultrasound, or any other suitablebiosignal), dynamic signals, non-destructive testing signals, conditionmonitoring signals, fluid signals, geophysical signals, astronomicalsignals, electrical signals, financial signals including financialindices, sound and speech signals, chemical signals, meteorologicalsignals including climate signals, and/or any other suitable signal,and/or any combination thereof.

In one embodiment, a PPG signal may be transformed using a continuouswavelet transform. Information derived from the transform of the PPGsignal (i.e., in wavelet space) may be used to provide measurements ofone or more physiological parameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {t}}}}} & (9)\end{matrix}$

where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (9) may be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation. Wavelets arecomposed of a range of frequencies, one of which may be denoted as thecharacteristic frequency of the wavelet, where the characteristicfrequency associated with the wavelet is inversely proportional to thescale a. One example of a characteristic frequency is the dominantfrequency. Each scale of a particular wavelet may have a differentcharacteristic frequency. The underlying mathematical detail requiredfor the implementation within a time-scale can be found, for example, inPaul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor &Francis Group 2002), which is hereby incorporated by reference herein inits entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms.Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined as

S(a,b)=|T(a,b)²  (10)

where ‘∥’ is the modulus operator. The scalogram may be rescaled foruseful purposes. One common rescaling is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{{{T\left( {a,b} \right)}}^{2}}{a}} & (11)\end{matrix}$

and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. Also included as a definition of a ridge hereinare paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the planeare labeled a “maxima ridge”.

For implementations requiring fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and then multiplying the resultby √{square root over (a)}.

In the discussion of the technology which follows herein, the“scalogram” may be taken to include all suitable forms of rescalingincluding, but not limited to, the original unscaled waveletrepresentation, linear rescaling, any power of the modulus of thewavelet transform, or any other suitable rescaling. In addition, forpurposes of clarity and conciseness, the term “scalogram” shall be takento mean the wavelet transform, T(a,b) itself, or any part thereof. Forexample, the real part of the wavelet transform, the imaginary part ofthe wavelet transform, the phase of the wavelet transform, any othersuitable part of the wavelet transform, or any combination thereof isintended to be conveyed by the term “scalogram”.

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & (12)\end{matrix}$

where f_(c), the characteristic frequency of the mother wavelet (i.e.,at a=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Morletwavelet, is defined as:

ψ(t)=π^(−1/4)(e ^(i2πf) ⁰ ^(t) −e ^(−(2πf) ⁰ ⁾ ² ^(/2))e ^(−t) ²^(/2)  (13)

where f₀ is the central frequency of the mother wavelet. The second termin the parenthesis is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi (t)} = {\frac{1}{\pi^{1/4}}^{\; 2\; \pi \; f_{0}t}^{{- t^{2}}/2}}} & (14)\end{matrix}$

This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof equation (14) is not strictly a wavelet as it has a non-zero mean(i.e., the zero frequency term of its corresponding energy spectrum isnon-zero). However, it will be recognized by those skilled in the artthat equation (14) may be used in practice with f₀>>0 with minimal errorand is included (as well as other similar near wavelet functions) in thedefinition of a wavelet herein. A more detailed overview of theunderlying wavelet theory, including the definition of a waveletfunction, can be found in the general literature. Discussed herein ishow wavelet transform features may be extracted from the waveletdecomposition of signals. For example, wavelet decomposition of PPGsignals may be used to provide clinically useful information within amedical device.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a rescaled wavelet space. For example, the pulsecomponent of a PPG signal produces a dominant band in wavelet space ator around the pulse frequency. FIGS. 3( a) and (b) show two views of anillustrative scalogram derived from a PPG signal, according to anembodiment. The figures show an example of the band caused by the pulsecomponent in such a signal. The pulse band is located between the dashedlines in the plot of FIG. 3( a). The band is formed from a series ofdominant coalescing features across the scalogram. This can be clearlyseen as a raised band across the transform surface in FIG. 3( b) locatedwithin the region of scales indicated by the arrow in the plot(corresponding to 60 beats per minute). The maxima of this band withrespect to scale is the ridge. The locus of the ridge is shown as ablack curve on top of the band in FIG. 3( b). By employing a suitablerescaling of the scalogram, such as that given in equation (11), theridges found in wavelet space may be related to the instantaneousfrequency of the signal. In this way, the pulse rate may be obtainedfrom the PPG signal. Instead of rescaling the scalogram, a suitablepredefined relationship between the scale obtained from the ridge on thewavelet surface and the actual pulse rate may also be used to determinethe pulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a rescaled wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary; varying in scale, amplitude, or both over time.FIG. 3( c) shows an illustrative schematic of a wavelet transform of asignal containing two pertinent components leading to two bands in thetransform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In this embodiment, the band ridge is defined as the locus ofthe peak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. This will be referred to as the “primary band”.In addition, it may be assumed that the system from which the signaloriginates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band Bthen the information within band B can become ambiguous (i.e., obscured,fragmented or missing). In this case, the ridge of band A may befollowed in wavelet space and extracted either as an amplitude signal ora scale signal which will be referred to as the “ridge amplitudeperturbation” (RAP) signal and the “ridge scale perturbation” (RSP)signal, respectively. The RAP and RSP signals may be extracted byprojecting the ridge onto the time-amplitude or time-scale planes,respectively. The top plots of FIG. 3( d) show a schematic of the RAPand RSP signals associated with ridge A in FIG. 3( c). Below these RAPand RSP signals are schematics of a further wavelet decomposition ofthese newly derived signals. This secondary wavelet decomposition allowsfor information in the region of band B in FIG. 3( c) to be madeavailable as band C and band D. The ridges of bands C and D may serve asinstantaneous time-scale characteristic measures of the signalcomponents causing bands C and D. This technique, which will be referredto herein as secondary wavelet feature decoupling (SWFD), may allowinformation concerning the nature of the signal components associatedwith the underlying physical process causing the primary band B (FIG. 3(c)) to be extracted when band B itself is obscured in the presence ofnoise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts. In one embodiment, there is an inversecontinuous wavelet transform which allows the original signal to berecovered from its wavelet transform by integrating over all scales andlocations, a and b:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi \left( \frac{t - b}{a} \right)}\ \frac{{a}\ {b}}{a^{2}}}}}}} & (15)\end{matrix}$

which may also be written as:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}{\psi_{a,b}(t)}\frac{{a}\ {b}}{a^{2}}}}}}} & (16)\end{matrix}$

where C_(g) is a scalar value known as the admissibility constant. It iswavelet type dependent and may be calculated from:

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{{{\hat{\psi}(f)}}^{2}}{f}\ {f}}}} & (17)\end{matrix}$

FIG. 3( e) is a flow chart of illustrative steps that may be taken toperform an inverse continuous wavelet transform in accordance with theabove discussion. An approximation to the inverse transform may be madeby considering equation (15) to be a series of convolutions acrossscales. It shall be understood that there is no complex conjugate here,unlike for the cross correlations of the forward transform. As well asintegrating over all of a and b for each time t, this equation may alsotake advantage of the convolution theorem which allows the inversewavelet transform to be executed using a series of multiplications. FIG.3( f) is a flow chart of illustrative steps that may be taken to performan approximation of an inverse continuous wavelet transform. It will beunderstood that any other suitable technique for performing an inversecontinuous wavelet transform may be used in accordance with the presentdisclosure.

FIG. 4 is an illustrative continuous wavelet processing system inaccordance with an embodiment. In this embodiment, input signalgenerator 410 generates an input signal 416. As illustrated, inputsignal generator 410 may include oximeter 420 coupled to sensor 418,which may provide as input signal 416, a PPG signal. It will beunderstood that input signal generator 410 may include any suitablesignal source, signal generating data, signal generating equipment, orany combination thereof to produce signal 416. Signal 416 may be anysuitable signal or signals, such as, for example, biosignals (e.g.,electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal), dynamic signals, non-destructive testingsignals, condition monitoring signals, fluid signals, geophysicalsignals, astronomical signals, electrical signals, financial signalsincluding financial indices, sound and speech signals, chemical signals,meteorological signals including climate signals, and/or any othersuitable signal, and/or any combination thereof.

In this embodiment, signal 416 may be coupled to processor 412.Processor 412 may be any suitable software, firmware, and/or hardware,and/or combinations thereof for processing signal 416. For example,processor 412 may include one or more hardware processors (e.g.,integrated circuits), one or more software modules, computer-readablemedia such as memory, firmware, or any combination thereof. Processor412 may, for example, be a computer or may be one or more chips (i.e.,integrated circuits). Processor 412 may perform the calculationsassociated with the continuous wavelet transforms of the presentdisclosure as well as the calculations associated with any suitableinterrogations of the transforms. Processor 412 may perform any suitablesignal processing of signal 416 to filter signal 416, such as anysuitable band-pass filtering, adaptive filtering, closed-loop filtering,and/or any other suitable filtering, and/or any combination thereof.

Processor 412 may be coupled to one or more memory devices (not shown)or incorporate one or more memory devices such as any suitable volatilememory device (e.g., RAM, registers, etc.), non-volatile memory device(e.g., ROM, EPROM, magnetic storage device, optical storage device,flash memory, etc.), or both. The memory may be used by processor 412to, for example, store data corresponding to a continuous wavelettransform of input signal 416, such as data representing a scalogram. Inone embodiment, data representing a scalogram may be stored in RAM ormemory internal to processor 412 as any suitable three-dimensional datastructure such as a three-dimensional array that represents thescalogram as energy levels in a time-scale plane. Any other suitabledata structure may be used to store data representing a scalogram.

Processor 412 may be coupled to output 414. Output 414 may be anysuitable output device such as, for example, one or more medical devices(e.g., a medical monitor that displays various physiological parameters,a medical alarm, or any other suitable medical device that eitherdisplays physiological parameters or uses the output of processor 412 asan input), one or more display devices (e.g., monitor, PDA, mobilephone, any other suitable display device, or any combination thereof),one or more audio devices, one or more memory devices (e.g., hard diskdrive, flash memory, RAM, optical disk, any other suitable memorydevice, or any combination thereof), one or more printing devices, anyother suitable output device, or any combination thereof.

It will be understood that system 400 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal generator 410 may beimplemented as parts of sensor 12 and monitor 14 and processor 412 maybe implemented as part of monitor 14.

The components and methods described herein may be used to generate oneor more scalograms using a wavelet transform as described above or anyother suitable method. Characteristics of the generated scalograms maybe identified and used for, among other things, identifying a conditionof a patient, such as a low perfusion condition, high vascularperipheral resistance, or other condition. When such conditions areidentified, an alert or other event may be triggered.

A scalogram derived from a healthy individual in a steady statecondition may have a dominant pulse band with low amplitudes adjacent tothe pulse band. FIG. 3( c), which is discussed above, shows anillustrative scalogram of a signal. If a PPG signal were used togenerate the scalogram in FIG. 3( c), band A may be the pulse band andband B may be the respiration band. Pulse band A in FIG. 3( c) is anexample of a dominant band with low amplitudes adjacent to the pulseband.

A scalogram derived from a person that may be experiencing a medicalcondition or problem, such as low perfusion, may have differentcharacteristics than a scalogram derived from a person who is notexperiencing such a condition or problem. A low perfusion condition,which may be caused by increased vascular peripheral resistance, maycause changes in blood flow and pulse rates that may be detected in thescalogram. For example, a low perfusion condition may cause regionsadjacent to the pulse band (e.g., above, below, or both above and belowthe pulse band) to contain relatively higher energy as the pulse signalbecomes weaker. These regions may be referred to as marker regions. Themarker regions may be spaced apart from the pulse band or may be anextension of the pulse band. Consecutive marker regions on the scalogrammay be evenly spaced apart in time, randomly spaced apart in time, orthe spacing may change over time. The marker regions may have anysuitable shape such as, for example, rectangular, oval, square,circular, triangular, or a combination of shapes. In one example, themarker regions may be narrower in time, and longer in scale.

The marker regions may be identified in a scalogram using any suitabletechnique. In general, a scalogram is generated by a processor, such asprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2), based on signaldata received from an oximeter (such as oximeter 14 (FIG. 1) or 420(FIG. 4)) or a sensor (such as a sensor 418 (FIG. 4) or sensor 12 (FIG.1)) that is located on a patient. The marker regions may be identifiedusing the processor 412 (FIG. 4) or microprocessor 48 (FIG. 2) byanalyzing the scalogram, as further described herein. In an embodiment,the marker regions and their sizes and shapes may be identified by theprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2) using an amplitudethreshold. The amplitude threshold may be predetermined or maydynamically change (e.g., as a function of the height and/or shape ofthe pulse band). The threshold may be selected by a user (e.g., via userinput 56 (FIG. 2)), or programmed for the processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2), and may be based in part on patientinformation, such as patient classification. In another embodiment, themarker regions may be identified by the processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2), other component, or user, based on a changein energy within one or more regions in the scalogram over time. Forexample, a rectangular region may be used and the energy within theregion may be determined using any suitable methods such as by taking amedian or average amplitude within the region or summing the amplitudeswithin the region. For example, a percentile of energies in a region maybe used to provide a measure of background noise for comparison with thepulse band. A marker region may also be identified by the processor 412(FIG. 4) or microprocessor 48 (FIG. 2) based on an increase andsubsequent decrease in energy within the region over time. The markerregions may also be identified by using a combination of techniques bythe processor 412 (FIG. 4) or microprocessor 48 (FIG. 2) or othercomponent.

A low perfusion condition may also result in a signal that causes theamplitudes in marker regions above and below the pulse band to increase.Changes in these amplitudes may be detected and categorized by theprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2). In an embodiment,the amplitudes may be detected by analyzing a defined region aboveand/or below the pulse band over time. The defined region may be definedby the processor 412 (FIG. 4) based on one or more features of thescalogram, selected by a user (via user input 56 (FIG. 2)) or programmedin processor 412 (FIG. 4). The region may be analyzed by calculating theenergy within the region or by using any other suitable technique. A lowperfusion condition may also cause the amplitude of the pulse band todecrease. Any decrease in pulse band amplitude may be detected andanalyzed over time by the processor 412 (FIG. 4) or microprocessor 48(FIG. 2).

FIG. 5 is an illustrative method for identifying a low perfusioncondition, or other condition, according to an embodiment. At step 510,one or more signals may be received by the processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2). The received signal may be a PPG signal(e.g., a red and/or infrared signal), or other signal described herein,which may be transmitted by an oximeter (such as oximeter 14 of FIG. 1or 420 of FIG. 4), sensor (such as sensor 418 of FIG. 4 or sensor 12 ofFIG. 1), or other device, and may be transmitted directly (via cables 24(FIG. 1)) to a processor (412 (FIG. 4) or microprocessor 48 (FIG. 2)),via an intermediary component, or using any appropriate transmissionmeans. At step 515, the received signal may be used by the processor 412(FIG. 4) or microprocessor 48 (FIG. 2) to generate a scalogram using awavelet transform (e.g., a continuous wavelet transform), or othertechnique described herein or known to one of skill in the art. Thescalogram may be any scalogram, such as the scalograms depicted in FIGS.3( a)-(b) and FIG. 6( a).

FIGS. 6( a)-(c) depict an example of a weakening pulse signal, which maybe observed in the signal plot above the scalogram of FIG. 6( a) and inthe scalogram of FIG. 6( a) which shows regions adjacent to a pulse band(A) that contain relatively higher energy (B) in proportion to the pulseband (A) as the pulse signal weakens, for example in a low perfusioncondition. As shown, FIG. 6( a) depicts a scalogram for a signal (shownat the top of the scalogram) where the pulse signal weakens. A pulsesignal may weaken because of a change in a physiological condition, suchas low perfusion. It may also weaken if a sensor (such as sensor 418(FIG. 4) or sensor 12 (FIG. 1)) is moved from one location to anotherless optimal location, or if the sensor is loosened. Weakening a pulsesignal in such ways may cause a pulse signal to reduce in amplitude.Other system noise (such as a mains hum, thermal noise, or other noise),however, typically remains constant.

The scalogram depicted in FIG. 6( a) may be generated based on theplotted pulse signal of FIG. 6( a) using techniques described herein andanalyzed using techniques described herein, such as those discussed inconnection with FIG. 5. The pulse signal may manifest itself in theassociated wavelet scalogram as a pulse band (a distinct band across thetransform plane) (marked A). Mains noise (or other noise) may manifestitself as one or more streaks (or other shape) across the scalogram athigher frequencies (marked B in the scalogram). Low amplitude signalnoise from, for example, thermal noise, may be present in one or morelocations in the scalogram at low energy values.

In one embodiment, in order to quantify a relative value of signal andnoise, a marker region defined as a window that may be ten seconds long(or other time period), and of various widths was scanned across thescalogram and representative energies computed as described below inconnection with FIGS. 6( b) and 6(c).

FIG. 6( b) shows: (1) a sum of energy densities within a ten secondwindow localized to scales in a region of the pulse band (line A of FIG.6( b)), (2) a sum of energy densities within a 10 second windowlocalized to scales in the region of a mains hum artifact (line B ofFIG. 6( b)) and (3) the lowest tenth percentile of energy values in theten second window run across a region between the mains hum artifact andthe pulse band (line C of FIG. 6( b)). The tenth percentile may be takenas a marker region characteristic. In one embodiment, other measures(marker region characteristics) or identifiable features (residualmarkers) may be interrogated or used. The plots shown in FIG. 6( b) maybe produced using one or more processors, such as 412 (FIG. 4) ormicroprocessor 48 (FIG. 2).

To derive a measure of a pulse signal and noise levels, a pulse band'srepresentative energy may be divided by a mains noise representativeenergy (line D in FIG. 6( c)), and the pulse band's representativeenergy may also be divided by the representative energy of low levelnoise (line E of FIG. 6( c)). Both of lines D and E show a decreasingtrend indicative of the signal reducing relative to background noise.Such a decreasing trend may be parameterized through, for example, curvefitting, including a linear straight line fit or a nonlinear curve fit.In this way a measure of the signal quality may be obtained usingwavelet transforms. This measure may be an absolute measure, a relativemeasure or an indication of trending over time. The plots shown in FIG.6( c) may be produced using one or more processors, such as 412 (FIG. 4)or microprocessor 48 (FIG. 2).

Embodiments of the measures, processing, and calculations described withreference to FIGS. 6( a)-(c) may also be provided using any suitablepercentiles, window lengths, and widths, to derive representativeenergies. Representative energies may be derived in other ways such astaking a peak value in time along a pulse band maximum (i.e. its ridge).In addition, other parts of a transform may be taken or used, such as areal part, imaginary part, various powers of the modulus, and the phase.

Another exemplary simplified scalogram is depicted in FIG. 6( d). FIG.6( d) shows a simplified scalogram 600 derived from a PPG signal.Scalogram 600 depicts pulse band 615, marker regions 618 adjacent to thepulse band 615, and residual markers 620 on either side of pulse band615. For simplicity, scalogram 600 does not depict other featurestypically found in a scalogram of a PPG signal (e.g., the respirationband, noise, etc.). The scalogram 600 may be generated during a lowperfusion condition. Characteristics of the residual markers 620 may bedetected and analyzed by the processor 412 (FIG. 4) or microprocessor 48(FIG. 2) to determine that there is a low perfusion condition.

Turning again to FIG. 5, at step 520, the scalogram pulse band may beidentified. The pulse band may be identified by the processor (412 (FIG.4) or microprocessor 48 (FIG. 2)) based at least in part on thescalogram data and/or the received signal from the oximeter (14 (FIG. 1)or 420 (FIG. 4)) or sensor (418 (FIG. 4) or 12 (FIG. 1)). For example,the pulse band may be identified using ridge following techniques on thescalogram, or via input for a pulse rate or interbeat time periods tolocate pulse in the scalogram, or other appropriate technique. At step530, the processor (412 (FIG. 4) or microprocessor 48 (FIG. 2)) or othercomponent may also be used to detect one or more marker regions adjacentto the pulse band. The marker regions may be identified by the processor(412 (FIG. 4) or microprocessor 48 (FIG. 2)) using, for example, ridgeor modulus maxima techniques, or other techniques well known to thoseskilled in the art. Marker regions may also be a defined region setforth by a user.

Characteristics of the marker region may be identified at step 535 usingthe processor (412 (FIG. 4) or microprocessor 48 (FIG. 2)). Someexamples of characteristics of marker regions include, for example,residual markers that may be detected adjacent to the pulse band, spacedapart from the pulse band, or as an extension of the pulse band, changesin the pulse band and/or the marker regions. Residual markers may beisolated regions or may be a continuous region having increased ordecreased amplitude. The size, shape, location, and amplitude of theresidual markers may be determined by the processor (412 (FIG. 4) ormicroprocessor 48 (FIG. 2)) based on the scalogram data. A residualmarker may be an identifiable feature in a marker region. Marker regionsare typically regions of arbitrary shape above and/or below the pulseband. Marker region characteristics may describe characteristic measuressuch as a 10^(th) percentile (or other percentile) of energy, forexample, as used in the example of FIGS. 6( a)-(c).

Characteristics of the marker regions may also be identified accordingto the process flowchart depicted in FIG. 7. At step 700, a markerregion may be identified in a manner similar to that described at step530 (FIG. 5). The marker region may be identified using the processor(412 (FIG. 4) or microprocessor 48 (FIG. 2)) via ridge or modulus maximatechniques, or other techniques well known to those skilled in the art.In some embodiments, marker regions may be a user-defined region.Identifying characteristics of the marker region include, for example,determining energy of the marker region at step 710 and determiningamplitude of the marker region at step 720. The energy and amplitude ofthe marker region may be obtained by processing scalogram data viaprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2).

In some embodiments, residual markers may also be detected at step 730.The residual markers may also be detected by processor 412 (FIG. 4) ormicroprocessor 48 (FIG. 2) using ridge or modulus maxima techniques, orother techniques. Certain features of the residual markers may also bedetected by the processor 412 (FIG. 4) or microprocessor 48 (FIG. 2),such as counting a number of residual markers at step 740, determining alocation of the residual markers at step 750, determining a size of theresidual markers at step 760, determining energy of the residual markersat step 770, determining amplitude of the residual markers at step 780,and determining the strength of the residual markers at step 790.

Referring again to FIG. 5, at step 540 a threshold for marker regioncharacteristics may be received. The threshold may be selected by a userusing user input 56 (FIG. 2) (or other input means), or programmed inprocessor 412 (FIG. 4) or microprocessor 48 (FIG. 2). The processor 412(FIG. 4) or microprocessor 48 (FIG. 2) may determine whether the markerregion characteristics correspond to the threshold at step 545. Thethreshold may be any combination of characteristics of the markerregions and residual markers. The number and characteristics of residualmarkers caused by low perfusion may vary for different people ordifferent groups or classes of people. Therefore, thresholds used toprovide an indication of low perfusion may also vary for differentpeople, groups or classes of people. Accordingly, in an embodiment thesystem (via processor programming) or operator (via user inputs) mayclassify a patient (e.g., based on age, health condition, heart rate,body position, etc.) and the system may trigger events based at least inpart on the user's classification.

The existence of residual markers (e.g., the existence of residualmarkers outside of the pulse band or as an extension of the pulse band)having certain characteristics has been found to indicate that the PPGsignal was obtained from an oximeter (18 (FIG. 1)) or sensor (12 (FIG.1)) located on a site having low perfusion (e.g., from high vascularperipheral resistance) or other problem. Thus, in some embodiments, ifat step 545 the residual marker features are determined to notcorrespond to a threshold, at step 550 an event may trigger. In general,not corresponding to the threshold may include being substantiallydissimilar to the threshold, exceeding or failing to meet the threshold.The event that may be triggered may be, for example, an alert or alarmthat signals existence of a low perfusion condition or other condition.Other examples of alerts or events may also be triggered, for example,notifications indicating: examination of a patient is necessary,movement of the sensor is required, examination of the sensor isrequired, or other notification. Alerts may be of any type, such as anaudible noise, lighted indicator, message, visual display, or otheralarm. In addition to (or instead of) the alert, the event may be tomove the sensor or oximeter (by a user operator, or using a controlsignal from the processor 412 (FIG. 4) sent to a wheel or rollerassembly integrated in the oximeter), switch use of the sensor to asecond sensor, or other event. In general, the events are triggered bythe processor 412 (FIG. 4) or microprocessor 48 (FIG. 2) sending acontrol signal to another system component, such as displays 20 or 28(FIG. 1), speaker 22 (FIG. 1), sensor 12 (FIG. 1), oximeter 16 (FIG. 1),or other component.

In some embodiments, steps 510-530 are repeated at step 555 as a followup routine to determine, for example, whether the event at step 550improved monitoring conditions. New signal information may be used togenerate a new scalogram in which new marker region characteristics maybe detected by the processor 412 (FIG. 4) or microprocessor 48 (FIG. 2).At step 560, the new scalogram marker region characteristics may becompared against the original scalogram and/or the threshold (receivedat step 540). If the new scalogram marker region characteristicscorrespond to the threshold at step 565, the routine may end. However,if the new scalogram marker region characteristics do not correspond tothe threshold, another event may trigger at step 570. The second eventmay be one or more of the same or different events discussed withreference to step 555. For example, a first event triggered at step 555may be a flashing light indicator, and a second event triggered at step570 may be a message, audible alarm, and a flashing light. Other events,and combinations of events, may also be used. Following step 570, thesystem may end the routine, or optionally repeat the steps at step 555 ntimes.

In an embodiment, the system implementing the foregoing methods andtechniques may also be used to calculate oxygen saturation. The oxygensaturation may be calculated using scalograms derived from PPG signals.For example, oxygen saturation may be calculated using the methodsdescribed in Addison et al. U.S. Patent Publication No. 2006/0258921,published Nov. 16, 2006. In an embodiment, during periods of lowperfusion, the oxygen saturation may continue to be calculated using thescalograms. A reliable oxygen saturation value may continue to becalculated because low perfusion may affect scales within the scalogramwithout significantly affecting the pulse band. The methods and systemmay also be used to identify other types of conditions, as would berecognized by one of skill in the art.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications can be made by those skilled in theart without departing from the scope and spirit of the disclosure.

What is claimed is:
 1. A system comprising: a signal generator forgenerating a signal; a processor coupled to the signal generator,wherein the processor is configured to perform operations comprising:generating a scalogram based at least in part on a wavelet transform ofthe signal; identifying a pulse band in the scalogram; identifying amarker region in the scalogram based at least in part on the position ofthe pulse band in the scalogram; identifying a characteristic of themarker region; and determining, based at least in part on thecharacteristic of the marker region, an indication of perfusion.
 2. Thesystem of claim 1, wherein the marker region is substantially parallelto the pulse band.
 3. The system of claim 1, wherein the pulse band isbetween portions of the marker region.
 4. The system of claim 1, whereinthe marker region is adjacent to the pulse band.
 5. The system of claim1, wherein the marker region is spaced apart from the pulse band.
 6. Thesystem of claim 1, wherein the marker region is positioned above, below,or both above and below the pulse band.
 7. The system of claim 1,wherein identifying the marker region comprises identifying the markerregion based at least in part on user input, a ridge of the scalogram,or a modulus maximum of the scalogram.
 8. The system of claim 1, whereinthe indication of perfusion is determined based at least in part on theenergy of the marker region.
 9. The system of claim 1, wherein thesignal comprises a photoplethysmograph signal from a subject.
 10. Thesystem of claim 1, wherein the characteristic of the marker region isselected from the group consisting of: a change in energy within themarker region over time, a change in amplitude within the marker regionover time, a residual marker, a number of residual markers, a size ofthe residual marker, a location of the residual marker, energy of theresidual marker, amplitude of the residual marker, strength of theresidual marker, and any combination thereof.
 11. The system of claim 1,wherein the processor is configured to perform operations furthercomprising receiving a threshold for a characteristic of the markerregion.
 12. The system of claim 11, wherein the threshold is based atleast in part on a user classification.
 13. The system of claim 11,wherein the processor is configured to perform operations furthercomprising: comparing the characteristic of the marker to the threshold;and triggering an event based at least in part on the comparison.
 14. Amethod comprising: receiving, using a processor, a signal generating,using the processor, a scalogram based at least in part on a transformof the signal; identifying, using the processor, a pulse band in thescalogram; identifying, using the processor, a marker region in thescalogram based at least in part on the position of the pulse band inthe scalogram; identifying, using the processor, a characteristic of themarker region; determining, using the processor, based at least in parton the characteristic of the marker region, an indication of perfusion.15. The method of claim 14, wherein identifying the marker regioncomprises identifying a region substantially parallel to the pulse band.16. The method of claim 14, wherein identifying the pulse band comprisesidentifying a band between portions of the marker region.
 17. The methodof claim 14, wherein identifying the marker region comprises identifyinga region adjacent to the pulse band.
 18. The method of claim 14, whereinidentifying the marker region comprises identifying a region spacedapart from the pulse band.
 19. The method of claim 14, whereinidentifying the marker region comprises identifying a region positionedabove, below, or both above and below the pulse band.
 20. The method ofclaim 14, wherein identifying the marker region comprises identifying aregion based at least in part on user input, a ridge of the scalogram,or a modulus maximum of the scalogram.
 21. The method of claim 14,wherein determining an indication of perfusion comprises determining anindication based at least in part on the energy of the marker region.22. The method of claim 14, wherein receiving the signal comprisesreceiving a photoplethysmograph signal from a subject.
 23. The method ofclaim 14, wherein identifying the characteristic of the marker regioncomprises identifying a characteristic selected from the groupconsisting of: a change in energy within the marker region over time, achange in amplitude within the marker region over time, a residualmarker, a number of residual markers, a size of the residual marker, alocation of the residual marker, energy of the residual marker,amplitude of the residual marker, strength of the residual marker, andany combination thereof.
 24. The method of claim 14, further comprisingreceiving a threshold for a characteristic of the marker region.
 25. Themethod of claim 24, wherein receiving the threshold comprises receivinga threshold based at least in part on a user classification.
 26. Themethod of claim 24, further comprising: comparing the characteristic ofthe marker to the threshold; and triggering an event based at least inpart on the comparison.